Inductive Transfer Learning for Handling Individual Differences in Affective Computing

Abstract

Although psychophysiological and affective computing approaches may increase facility for development of the next generation of human-computer systems, the data resulting from research studies in affective computing include large individual differences. As a result, it is important that the data gleaned from an affective computing system be tailored for each individual user by re-tuning it using user-specific training examples. Given the often time-consuming and/or expensive nature of efforts to obtain such training examples, there is a need to either 1) minimize the number of user-specific training examples required; or 2) to maximize the learning performance through the incorporation of auxiliary training examples from other subjects. In [11] we have demonstrated an active class selection approach for the first purpose. Herein we use transfer learning to improve the learning performance by combining user-specific training examples with auxiliary training examples from other subjects, which are similar but not exactly the same as the user-specific training examples. We report results from an arousal classification application to demonstrate the effectiveness of transfer learning in a Virtual Reality Stroop Task designed to elicit varying levels of arousal.